Project risks come mainly from the future uncertainties, and new product and service development projects possess the most uncertainty and therefore the highest risk. However, if the project team has gained experiences from previous similar projects, the risk to the project drastically declines. In other words, an inverse correlation exists between member experiences and project risk. Therefore, the guiding principle would be to assign an experienced member to execute familiar work for that member. This study aims to establish a mathematical model that uses member experience to minimize project uncertainty. The nonlinear model assigns members with different experiences to the most suitable tasks to minimize project risk, and thus maximize project success rates under constraints of project costs and communication complexities. An experimental case is used to demonstrate the applicability of the proposed model, and results indicated that under cost and communication complexity constraints, project uncertainty can truly be minimized by allocating an adequate member to the suitable task.

To alleviate high energy dissipation of unnecessary snooping accesses, snoop filters have been designed to reduce snoop lookups. These filters have the problem of decreasing filtering efficiency, and thus usually rely on partial or whole filter reset by detecting block evictions. Unfortunately, the reset conditions occur infrequently or unevenly (called passive filter deletion ). This work proposes the concept of revitalized snoop filter (RSF) design, which can actively renew the destination filter by employing a generation wrapping-around scheme for various reference behaviors. We further utilize a sampling mechanism for RSF to timely trigger precise filter revitalizations, so that unnecessary RSF flushing can be minimized. The proposed RSF can be integrated to various existent inclusive snoop filters with only a minor change to their designs. We evaluate our proposed design and demonstrate that RSF eliminates 58.6% of snoop energy compared to JETTY on average while inducing only 6.5% of revitalization energy overhead. In addition, RSF eliminates 45.5% of snoop energy compared to stream registers on average and only induces 2.5% of revitalization energy overhead. Overall, these RSFs reduce the total L2 cache energy consumption by 52.1% (58.6% -- 6.5%) as compared to JETTY and by 43% (45.5% -- 2.5%) as compared to stream registers. Furthermore, RSF improves the overall performance by 1% to 1.4% on average compared to JETTY and stream registers for various benchmark suites.

Regular expression matching becomes indispensable elements of Internet of Things network security. However, traditional ternary content addressable memory (TCAM) search engine is unable to handle patterns with wildcards, as it precisely tracks only one active state with single transition. This paper proposes a promising simultaneous pattern matching methodology for wildcard patterns by two separated engines to represent discrete finite automata. A key preprocessing to encode possible postfix pattern by a unique key ensures that follow-up patterns can accurately traverse all possible matches with limited hardware resources. This approach is practical and scalable for achieving good performance and low space consumption in network security, and it can be applicable to any regular expressions even with multiwildcard patterns. The experimental results demonstrate that this scheme can efficiently and accurately recognize wildcard patterns by simultaneously tracking only two active states. By adopting SRAM TCAM in the proposed architecture, the energy consumption is reduced to around 39%, compared with the energy consumption using a computing system that contains a large memory lookup and comparison overhead.

Ternary content-addressable memory (TCAM)-based search engines generally need a priority encoder (PE) to select the highest priority match entry for resolving the multiple match problem due to the don't care (X) features of TCAM. In contemporary network security, TCAM-based search engines are widely used in regular expression matching across multiple packets to protect against attacks, such as by viruses and spam. However, the use of PE results in increased energy consumption for pattern updates and search operations. Instead of using PEs to determine the match, our solution is a three-phase search operation that utilizes the length information of the matched patterns to decide the longest pattern match data. This paper proposes a promising memory technology called priority-decision in memory (PDM), which eliminates the need for PEs and removes restrictions on ordering, implying that patterns can be stored in an arbitrary order without sorting their lengths. Moreover, we present a sequential input-state (SIS) scheme to disable the mass of redundant search operations in state segments on the basis of an analysis distribution of hex signatures in a virus database. Experimental results demonstrate that the PDM-based technology can improve update energy consumption of nonvolatile TCAM (nvTCAM) search engines by 36%-67%, because most of the energy in these search engines is used to reorder. By adopting the SIS-based method to avoid unnecessary search operations in a TCAM array, the search energy reduction is around 64% of nvTCAM search engines.

TCAM-based search engines are widely used in regular expression matching across multiple packets. However, the use of priority encoder results in increased energy consumption of pattern updates and search operations. This work, proposes a promising memory technology, called Priority-Decision in Memory (PDM), which eliminates the need for priority encoders and removes restrictions on ordering, meaning that patterns can be stored in an arbitrary order without sorting their lengths. Moreover, we present a Sequential Input-State Search (SIS) scheme to disable the mass of redundant search operations in state segments, based on the analysis distribution of hex signatures in a virus database. Experimental results demonstrate that PDM-based technology can improve update energy consumption of nvTCAM search engines by 36%~67% because most of the energy in the latter is used to reorder. By adopting the SIS-based method to avoid unnecessarily search operations in a TCAM array, the search energy reduction is around 64% of nvTCAM search engines.

NVM has commonly been used to address increasingly large last-level caches (LLCs) requirements by reducing leakage. However, frequent data-writing operations result in increased energy consumption. In this context, a promising memory technology, Non-volatile SRAM (nvSRAM), enables normal and standby operation modes which can be used to store various types of data. However, nvSRAM suffers from high dynamic energy usage due to frequent switching between operation modes. In this paper, we propose a redundant store elimination (RSE) scheme which, on average, discards 94% of needless bit-write operations. Moreover, we present a retention-aware cache management policy to reduce data updates of cache blocks, based on the correlation between data lifetime and cache types. Experimental results demonstrate that our proposal can improve energy consumption of SRAM-based and RRAM-based LLCs by 57% and 31%, respectively.

Dalvik virtual machine in the Android system creates a profiling barrier between VM-space applications and Linux user-space libraries. It is difficult for existing profiling tools on the Android system to definitively identify whether a bottleneck occurred in the application level, the Linux user-space level, or the Linux kernel level. Information barriers exist between VM-space applications and Linux native analysis tools due to runtime virtual machines' dynamic memory allocation mechanism. Furthermore, traditional vertical profiling tools targeted for Java virtual machines cannot be simply applied on the Dalvik virtual machine due to its unique design. The proposed the Reconfigurable Vertical Profiling Framework bridges the information gap and streamlines the hardware-software co-design process for the Android runtime system.